class_docstr: Base class for seq2seq models with embeddings
    
docstr: Runs decoding based on the encoder outputs.
      
docstr_tok: ["Runs","decoding","based","on","the","encoder","outputs","."]
code: def decode(self, encoder_output, features, labels):
      """Runs decoding based on the encoder outputs."""
      raise NotImplementedError()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import collections
  import tensorflow
  from seq2seq import graph_utils
  from seq2seq import losses
  from seq2seq.contrib.seq2seq.decoder import _transpose_batch_time
  from seq2seq.data import vocab
  from seq2seq.graph_utils import templatemethod
  from seq2seq.decoders.beam_search_decoder import BeamSearchDecoder
  from seq2seq.inference import beam_search
  from seq2seq.models.model_base import ModelBase, _flatten_dict
label_desc: Use image node_id 9 for calling the Seq2SeqModel obj's underlying member method code with example usage: obj.decode(encoder_output, features, labels) without return types